def __init__(self, version, qid=None, model="act_mr3", cmb_type='LensedUnabberatedCMB', dobeam=True, add_foregrounds=True, apply_window=True, max_cached=1,extract_region = None,extract_region_shape = None,extract_region_wcs = None):
"""
version: The version identifier for the filename of covsqrts on disk
model: The name of an implemented soapack datamodel
extract_region: An optional map whose footprint on to which the sims are extracted
extract_region_shape: Instead of passing a map for extract_region, one can pass its shape and wcs
extract_region_wcs: Instead of passing a map for extract_region, one can pass its shape and wcs
max_cached: The maximum number of cached sim/or alms
"""
self.qid = qid
self.noise_gen = noise.NoiseGen(qid=qid,version=version,model=model,ncache=max_cached,verbose=False)
self.signal_gen = signal.SignalGen(cmb_type=cmb_type, dobeam=dobeam, add_foregrounds=add_foregrounds, apply_window=apply_window, max_cached=max_cached, model=model)
self.default_geometries = {}
self.model = model
if (extract_region is not None) or (extract_region_shape is not None):
self._refoot = True
if (extract_region is not None):
assert extract_region_shape is None
assert extract_region_wcs is None
extract_region_shape, extract_region_wcs = extract_region.shape, extract_region.wcs
assert extract_region_wcs is not None
self._eshape, self._ewcs = extract_region_shape, extract_region_wcs
else:
self._refoot = False
def get_default_geometry(version, season, patch, array, freq, model='act_mr3'):
noise_gen = noise.NoiseGen(version=version,
model=model,
ncache=1,
verbose=False)
oshape, owcs = noise_gen.load_covsqrt(season,
patch,
array,
coadd=True,
mask_patch=None,
get_geometry=True)
return (oshape, owcs)
# Get data model
dm = sints.models[args.model](region=mask)
# Get a NoiseGen model
if args.extract_mask is not None:
emask = sints.get_act_mr3_crosslinked_mask(mask_patch,
version=args.extract_mask,
kind=args.mask_kind,
season=season,
array=array + "_f150")
eshape, ewcs = emask.shape, emask.wcs
else:
emask = mask
ngen = noise.NoiseGen(qid=qid,
version=version,
model=args.model,
extract_region=emask,
ncache=1,
verbose=True)
# Get split data
splits = enmap.enmap([dm.get_splits(q) for q in qid])
nsplits = dm.ainfo(args.qid, 'nsplits')
# Get inverse variance map
ivars = enmap.enmap([dm.get_ivars(q) for q in qid])
modlmap = splits.modlmap()
if calc_covsqrt:
flatstring = "un" if args.do_only_filter_noise else ""
# Generate each ACT and Planck sim and store kdiffs,kcoadd in memory
set_id = args.set_id
bandpasses = not(args.effective_freq)
gconfig = io.config_from_yaml("input/data.yml")
mask = sints.get_act_mr3_crosslinked_mask(args.region,
version=args.mask_version,
kind='binary_apod')
shape,wcs = mask.shape,mask.wcs
Ny,Nx = shape
modlmap = enmap.modlmap(shape,wcs)
ngen = {}
ngen['act_mr3'] = actnoise.NoiseGen(args.sim_version,model="act_mr3",extract_region=mask,ncache=0,verbose=True)
ngen['planck_hybrid'] = actnoise.NoiseGen(args.sim_version,model="planck_hybrid",extract_region=mask,ncache=0,verbose=True)
arrays = args.arrays.split(',')
narrays = len(arrays)
nsims = args.nsims
jsim = pipeline.JointSim(arrays,args.fg_res_version+"_"+args.region,
bandpassed=bandpasses,no_act_color_correction=args.no_act_color_correction,
ccor_exp=args.ccor_exp)
comm,rank,my_tasks = mpi.distribute(nsims)
for task in my_tasks:
sim_index = task + args.start_index
kind="binary_apod",
season=season,
array=args.array + "_f150",
pad=args.mask_pad)
# print(mask.shape)
mask = mask[400:-400, 5000:-5000]
mask *= enmap.apod(mask, 400)
# print(mask.shape)
noise.plot(pout + "_mask", mask, grid=True)
dm = sints.models[model](region=mask)
# Get a NoiseGen model
emask = mask
ngen = noise.NoiseGen(version=version,
model=model,
extract_region=emask,
ncache=1,
verbose=True)
# Get arrays from array
splits = dm.get_splits(season=season,
patch=patch,
arrays=dm.array_freqs[args.array],
srcfree=True)
ivars = dm.get_splits_ivar(season=season,
patch=patch,
arrays=dm.array_freqs[args.array])
noise.plot(pout + "_splits", splits)
noise.plot(pout + "_ivars", ivars)
parser.add_argument("--mask-pad", type=int, default=None,
help='Mask additional padding. No padding is applied to the extracted mask if any specified.')
parser.add_argument("--extract-mask", type=str, default=None,
help='Make sims on the big mask but do all the analysis on an extract of this version.')
parser.add_argument("--binary-percentile", type=float, default=10.,help='Binary percentile for sim masking.')
parser.add_argument("--season", type=str,help='Season')
parser.add_argument("--array", type=str,help='Array')
parser.add_argument("--patch", type=str,help='Patch')
args = parser.parse_args()
mask_patch = args.patch if args.mask_patch is None else args.mask_patch
with bench.show("region"):
if args.extract_mask is not None:
region_map = sints.get_act_mr3_crosslinked_mask(mask_patch,
version=args.extract_mask,
kind=args.mask_kind,
season=args.season)
else:
region_map = None
with bench.show("ngen init"):
ngen = noise.NoiseGen(args.version,extract_region=region_map,ncache=1)
with bench.show("make a sim"):
ngen.generate_sim(season=args.season,patch=args.patch,array=args.array,seed=1)
with bench.show("make another sim"):
ngen.generate_sim(season=args.season,patch=args.patch,array=args.array,seed=2)